A-posteriori error estimates for optimal control problems with state and control constraints

Transcription

1 A-posteriori error estimates for optimal control problems with state and control constraints A. Rösch, D. Wachsmuth RICAM-Report

2 A-POSTERIORI ERROR ESTIMATES FOR OPTIMAL CONTROL PROBLEMS WITH STATE AND CONTROL CONSTRAINTS arnd rösch 1, daniel wachsmuth 2, Abstract. We discuss the full discretization of an elliptic optimal control problem with pointwise control and state constraints. We provide the first reliable a-posteriori error estimator that contains only computable quantities for this class of problems. Moreover, we show, that the error estimator converges to zero if one has convergence of the discrete solutions to the solution of the original problem. The theory is illustrated by numerical tests. 1. Introduction. In this paper we consider the optimal control problem of minimizing the cost functional J given by subject to J(y, u = 1 2 y y d 2 L 2 (Ω + α 2 u 2 L 2 (Ω (1.1 y + y = u in Ω y = 0 on Γ, (1.2 and u a u u b a.e. in Ω, (1.3 Let us define the set of admissible controls by y a y y b in Ω. (1.4 U ad := {u L 2 (Ω : u a u u b a.e. in Ω}. In order to guarantee existence and regularity of solutions, we assume for the whole paper: Assumption 1. Ω R n, n {2, 3}, is a convex polygonal domain; u a < u b are constants; y a, y b C( Ω, y d L 2 (Ω, α 0. Note that our problem and also the discretized counterparts are strictly convex. Therefore, one has only to show the existence of a feasible point to get existence and uniqueness of an optimal control ū and state ȳ. Under a Slater type assumption we will state such a result in Theorem 2.3. Using the same assumption, one obtains existence and uniqueness of solutions (ȳ h, ū h for sufficiently fine discretizations. The distance of the solution (ȳ h, ū h to (ȳ, ū can be estimated a-priori as ū h ū L 2 (Ω + ȳ h ȳ H 1 (Ω Ch 2 n 2 ε, ε 0, see e.g. [8, 17]. In order to generate adaptively refined meshes, computable and localized a-posteriori error estimators are inevitable. For work on dual-weighted residual error indicators for state constrained problems we refer to [2, 10, 21]. The main drawback of this method is that the resulting 1 Universität Duisburg-Essen, Fachbereich Mathematik, Forsthausweg 2, D Duisburg, Germany, arnd.roesch@uni-due.de 2 Johann Radon Institute for Computational and Applied Mathematics (RICAM, Austrian Academy of Sciences, Altenbergerstraße 69, A-4040 Linz, Austria, daniel.wachsmuth@oeaw.ac.at 1

3 error estimators are not computable since they depend on the unknown solution of the continuous problem. The only work in context of residual error estimates is due to [14]. However, this error estimator contains also an uncomputable part that involves the Lagrange multipliers of the continuous problem. All cited papers contain different heuristics to replace uncomputable quantities by computable ones. Our main goal is to derive computable error bounds. The main difficulty to obtain a computable error bound is that the errors in the dual quantities p h p and µ i,h µ i cannot be majorized by ū h ū L2 (Ω. Hence, we completely avoid the use of the Lagrange multipliers and of the first-order necessary optimality system of Theorem 2.3. In our main result Theorem 3.9 we describe detailed all the different error contributions. Moreover, we prove that the error estimate converges to zero if the discrete quantities converge in a certain sense, see Section 4. Such a result does not seem to be available even for pure control constrained problems, see the comments at the end of that section. Let us shortly describe the structure of our paper. Section 2 contains basic properties of the optimal control problems and its discrete counterparts. A-posteriori error estimates are derived in Section 3. The behavior of the error bound when the discrete solutions converge is studied in Section 4. Numerical experiments are presented in Section Optimality system and discretization The undiscretized problem. First, let us define the notion of weak solutions of the state equation (1.2. A function y H 1 0(Ω is called weak solution of (1.2 if it satisfies the the weak formulation where the bilinear form a is defined as a(y, v = (u, v L2 (Ω v H0 1 (Ω, (2.1 a(y, v = ( y, v L 2 (Ω n + (y, v L 2 (Ω. Let us define the operators A : V := H 2 (Ω H 1 0(Ω L 2 (Ω by A = + I and its dual A : L 2 (Ω V by (Aw, v = w, A v w V, v L 2 (Ω. Lemma 2.1. For each control u L 2 (Ω the state equation (1.2 admits a unique weak solution y H 2 (Ω, and the mapping u y(u is continuous from L 2 (Ω to H 2 (Ω, i.e. y H2 (Ω C 0 u L2 (Ω. For the proof we refer to Grisvard [9]. Throughout the article we will assume the existence of a Slater point: Assumption 2. There exists û U ad and τ R, τ > 0, such that the associated state ŷ satisfies y a + τ ŷ y b τ. Please note, that this assumption implies that the state constraints cannot be active on Γ, i.e. it holds y a < τ and τ < y b on Γ since ŷ = 0 on Γ due to the Dirichlet boundary conditions. 2

4 Additionally, Assumption 2 implies that the feasible set of the control problem is non-empty. Due to convexity, we get immediately the existence and uniqueness of solutions. Lemma 2.2. Under Assumption 2, the optimal control problem (1.1 (1.4 admits a unique solution (ȳ, ū. The solution of the optimal control problem can be characterized by means of firstorder necessary optimality conditions. Due to Assumption 2, one can prove existence of Lagrange multipliers, see e.g. [5, 20]. In the following, let us denote by M(Ω = C( Ω the space of regular Borel measures. Theorem 2.3. Let (ȳ, ū be a solution of the problem (1.1 (1.3. Then there are µ a, µ b M(Ω and p W 1,s (Ω, s < n n 1, such that the following system is satisfied, which consists of adjoint equation p = ȳ y d µ a + µ b in Ω, p = 0 complementarity conditions for µ a and µ b and the variational inequality For the proof we refer to Casas [5]. on Γ = Ω, (2.2 µ i 0, µ i, ȳ y i = 0 i {a, b}, (2.3 (αū + p, u ū 0 u U ad. ( Discretization. Let us fix the assumptions on the discretization of problem (1.1-(1.4 by finite elements. First let us specify the notation of regular meshes. Each mesh T consists of closed cells T (for example triangles, tetrahedra, etc. such that Ω = T T T holds. We assume that the mesh is regular in the following sense: for different cells T i, T j T, i j, the intersection T i T j is either empty or a node, an edge, or a face of both cells, i.e. hanging nodes are not allowed. Let us denote the size of each cell by h T = diamt and define h(t = max T T h T. For each T T, we define R T to be the diameter of the largest ball contained in T. We will work with a family of regular meshes F = {T h } h>0, where the meshes are indexed by their mesh size, i.e. h(t h = h. We assume in addition that there exists a positive constant R such that h T R T R holds for all cells T T h and all h > 0. With each mesh T h F, we associate the finite-dimensional space V h H0 1 (Ω that consists of polynomial finite element functions of degree l 1. Furthermore, let us denote by U h L 2 (Ω the corresponding control discretization. Here, we have the following three possibilities in mind: discretization of controls by piecewise constant or linear finite element functions, or the choice U h = L 2 (Ω, which corresponds to the so-called variational discretization introduced by Hinze [12]. Let us now introduce the discretized version of the optimal control problem (1.1 (1.4. This problem is given as: Find y h V h and u h U h that minimize min J(y h, u h = 1 2 y h y d 2 L 2 (Ω + α 2 u h 2 L 2 (Ω (2.5 3

5 subject to the discretized state equation the control constraints and the discrete state constraints a(y h, v h = (u h, v h L 2 (Ω v h V h, (2.6 u a u h u b a.e. in Ω, (2.7 y a (x i y h (x i y b (x i for i = 1,.., K. (2.8 Here, we denoted by x i, i = 1...K, the nodes of the mesh T. Before proving existence of solutions of this problem, we first state some auxiliary results Lemma 2.4. Let y h and y h be the solution of (2.1 and (2.6 for the control u h. Let C M > 1 be given such that max h T C M min h T is satisfied. Then the following T T h T T h L -error estimate holds For a proof, we refer to Braess [3]. y h y h L (Ω ch u L 2 (Ω. Let us denote by Π h the L 2 (Ω-projection onto U h for piecewise constant functions. For piecewise linear functions one can use a quasi-interpolation, see [18]. In the case of the variational discretization we take Π h = I. In all three cases, this operator has the following known approximation property. Lemma 2.5. There is a constant c I independent of h such that is fulfilled for all u H 1 (Ω. u Π h u L 2 (Ω c I h u L 2 (Ω Regarding existence of solutions of the discrete optimal control problem, we have the following result. Lemma 2.6. Let C M > 1 be given and max h T C M min h T. Then, there exists a T T h T T h mesh size h 0 > 0 such that for all h h 0 a feasible point of the discretized problem exists. Proof. We set û h = Π h û, where û is the Slater point from Assumption 2. The function û h satisfies the control constraints. It remains to check the state constraints. Let us denote the solutions of the discrete and continuous state equation associated to û h by ŷ h and ŷ h, respectively. We find ŷ h (x i y a (x i τ ŷ(x i ŷ h (x i ŷ h (x i ŷ h (x i τ ŷ ŷ h L (Ω ŷ h ŷ h L (Ω Here, ŷ ŷ h L (Ω becomes small for small h because of Lemmas 2.5 and 2.1. The term ŷ h ŷ h L (Ω reflects the pointwise discretization error, which tends to zero for h 0 due to Lemma 2.4. Consequently, for sufficiently small h the discrete lower state constraints are fulfilled. Analogously, one shows that the upper state constraint of the discrete problem is satisfied for h small enough. Hence, the point (ŷ h, û h is admissible for the discrete problem. 4

6 As for the continuous problem, we get existence and uniqueness of solutions of the discrete problem. Lemma 2.7. Let Assumption 2 be satisfied and max h T C M min h T. Let the meshsize satisfy h < h 0. Then the optimal control problem (2.5 (2.8 admits a uniquely T T h T T h determined solution (ȳ h, ū h for all meshes with mesh-size h < h 0. Proof. Due to Lemma 2.6 the feasible set of the discrete problem is non-empty for h < h 0. By standard arguments one concludes the existence of a unique solution of this problem. Let us remark that the existence of a feasible point for the discrete problem is not guaranteed for arbitrary meshes with h < h 0. This is due to the fact that a-priori L -error estimates are derived using inverse inequalities. Then the relation between minimal and maximal element size must be limited by some constant C M. Another possibility are meshes for which a discrete maximum principle holds, see [7]. Usually, sequences of adaptive refined grids do not satisfy these requirements. However, the existence of a feasible point can be verified a-posteriori. If no feasible point exists, then one can refine the mesh in a suitable way to get a feasible point for the new mesh. We will use the following strategy. Our a-posteriori error estimator will contain a term that measures the L (Ω-norm of the violation of the state constraint, and thus should ensure the solvability of the discrete systems for the adaptively generated meshes. Assumption 3. In the sequel, we assume that the discrete optimal control problem admits a unique solution. Analogous to the continuous problem, one finds that the solution of the discrete problem can be characterized by a first-order optimality system. Theorem 2.8. Let (ȳ h, ū h be a solution of the problem (2.5 (2.8. Then there are µ a, µ b M(Ω and p h V h, such that the following system is satisfied, which consists of discrete adjoint equation a(v h, p h = (ȳ h y d, v h L 2 (Ω + µ a,h + µ b,h, v h v h V h, (2.9 complementarity condition µ i,h = K µ j i,h δ(x j, µ j i,h 0, µj i,h (ȳ h(x j y i (x = 0, i {a, b}, j {1,.., K}, j=1 and variational inequality (αū h + p h, u h ū h 0 u h U h U ad. (2.10 Since the state constraints for the discrete problem were prescribed in the mesh nodes, the Lagrange multipliers µ a,h and µ b,h are positive linear combinations of Dirac measures concentrated in the mesh nodes. Due to this representation, the complementarity condition in (2.10 can be written as µ i,h, ȳ h y i = 0 i {a, b}. 5

7 3. A-posteriori error estimates. In this section, we will derive an upper bound for the error ū h ū L 2 (Ω. In order to avoid the difficulties mentioned in the introduction, we will not work with the first-order optimality system given by Theorem 2.3. We will work with the optimality of ū instead, i.e. J(ȳ, ū J(y, u for all (y, u satisfying the constraints (1.2 (1.4. Let us suppose that we have computed the solution (ȳ h, ū h, p h, µ a,h, µ b,h of the discrete problem. At first, we have to find a pair (ỹ, ũ (ȳ h, ū h that is feasible for the continuous problem, which would give J(ȳ, ū J(ỹ, ũ Construction of feasible control. Let us first define an auxiliary state y h as the weak solution of the state equation with right-hand side ū h, i.e. a(y h, v = (ū h, v v H0 1 (Ω. (3.1 Lemma 3.1. Let û denote the Slater point as given by Assumption 2. Let y h denote the solution of (3.1. Let us define the violation of the state constraints by e sc := max( (ȳ h y a L (Ω, (y b ȳ h L (Ω. (3.2 Then the state ỹ := (1 σy h + σŷ associated to the control ũ := (1 σū h + σû is admissible for the state constraints (1.4 if σ is chosen as σ = ȳ h y h L (Ω + e sc τ + ȳ h y h L (Ω + e sc. Proof. We find (1 σy h + σŷ (1 σ(ȳ h ȳ h y h L (Ω + σ(y a + τ y a + τ + (1 σ(ȳ h ȳ h y h L (Ω y a τ y a + τ + (1 σ( (ȳ h y a L (Ω ȳ h y h L (Ω τ y a + τ (1 σ(τ + ȳ h y h L (Ω + e sc, which implies (1 σy h τ + σŷ y a for 1 σ τ+ ȳ h y h L (Ω +e sc. An analogous discussion for the upper state constraint yields the claim. With the notation of the previous lemma, we have ũ ū h = σ(û ū h, (3.3 which allows to estimate the difference ũ ū h L 2 (Ω provided upper bounds for σ are available. The difference in the states ỹ ȳ h can be written as ỹ ȳ h = ỹ y h + y h ȳ h = σ(ŷ y h + y h ȳ h = σ(ŷ ȳ h + (1 σ(y h ȳ h. (3.4 Remark 3.2. Please note, that we did not use feasibility of ȳ h for the discrete optimization problem. Hence, Lemma 3.1 is valid without this assumption, which means it is also applicable if the discrete optimization problem is solved for instance by penalty methods. 6

8 Remark 3.3. If the discrete space V h is the space of piecewise linear polynomial functions, then we can replace the constraint violation e sc by the interpolation error of the state constraint bounds. Let I h denote the Lagrange (or nodal interpolation operator. Then the discrete state constraints (2.8 imply I h y a ȳ h I h y b on Ω. And we can estimate in the previous proof (1 σy h + σŷ (1 σ(ȳ h ȳ h y h L (Ω + σ(y a + τ which shows that we can replace e sc by y a + τ + (1 σ(i h y a ȳ h y h L (Ω y a τ, ẽ sc := max( (I h y a y a L (Ω, (y b I h y b L (Ω. If the functions y a, y b can be exactly represented by piecewise linear functions then ẽ sc = 0. This is in particular the case if both state constraints are constant functions Estimate of error in the control variational inequality. As one ingredient of the final error estimator we will develop an error estimator for the error in the variational inequality (2.4. We will comment on the relation to existing work at the end of Section 4. At first, let us define the following subsets of Ω Ω 0,h := { x Ω : ū h (x, 1 α p h(x (u a, u b or ū h (x = u a, αū h (x + p h (x < 0 } or ū h (x = u b, αū h (x + p h (x > 0 (3.5 and Ω a,h := {x : ū h (x (u a, u b, 1α } p h(x u a Ω b,h := {x : ū h (x (u a, u b, 1α } p h(x u b. (3.6 The set Ω 0,h contains the points, where ū h and 1 α p h are strict between the bounds, and where ū h is at the bound but αū h + p h has the wrong sign. The sets Ω a,h and Ω b,h contain points, where ū h is strictly between the bounds, but 1 α p h is not feasible with respect to these bounds. In addition, we have the following properties αū h + p h α(ū h u a > 0 on Ω a,h, αū h + p h α(ū h u b < 0 on Ω b,h. (3.7 Lemma 3.4. Let ū h U ad and p h L 2 (Ω be given. Let the sets Ω 0,h, Ω a,h, and Ω b,h be defined according to (3.5 and (3.6. Then for each u U ad it holds (αū h + p h, u ū h (χ Ω0,h (αū h + p h, u ū h + (χ Ωa,h (αū h + p h, u a ū h + (χ Ωb,h (αū h + p h, u b ū h 7

9 Proof. The proof follows directly from the definition of the sets, and the properties of ū h and αū h + p h on these sets, confer also (3.7. In the derivation of the a-posteriori error estimate in Section 3.4, we will use the following implication of the previous lemma. Lemma 3.5. Let ū h U ad and p h L 2 (Ω be given. Let the sets Ω 0,h, Ω a,h, and Ω b,h be defined according to (3.5 and (3.6. Then for each u U ad it holds (αū h + p h, u ū h α 4 u ū h 2 L 2 (Ω η vi 2 L 2 (Ω, where η vi = η vi (ū h, p h L 2 (Ω is given by η 2 vi = 1 α χ Ω 0,h (αū h + p h 2 +χ Ωa,h (αū h + p h (ū h u a +χ Ωb,h (αū h + p h (ū h u b. (3.8 Proof. The claim follows directly from the definition of η vi and the inequality (χ Ω0,h (αū h + p h, u ū h α 4 u ū h 2 L 2 (Ω 1 α αū h + p h 2 L 2 (Ω 0,h. The function η vi will serve as a localizable error estimator for the error in the variational inequality Estimate of cost functional values with respect to the admissible point. Due to the feasibility of (ỹ, ũ the inequality 0 J(ỹ, ũ J(ȳ, ū holds. Now, we will derive an upper bound for J(ỹ, ũ J(y, u for arbitrary feasible pairs (y, u in terms of the distance u ũ L 2 (Ω and of residuals of the optimality system. Lemma 3.6. Let (ỹ, ũ be given by Lemma 3.1. Then it holds for all (y, u satisfying (1.2 (1.4 J(ỹ, ũ J(y, u α 4 u ũ 2 L 2 (Ω + η a u ũ L 2 (Ω + η b, where η a, η b are real numbers depending on (ȳ h, ū h, p h, µ a,h, µ b,h, (ỹ, ũ, and the data of the problem but not on (y, u, see below (3.13. Proof. Let us introduce the abbreviation r := u ũ L 2 (Ω. Since ỹ and y are solutions to the elliptic equations for right-hand sides ũ and u respectively, we have by Lemma 2.1 the regularity ỹ, y H 2 (Ω H0 1 (Ω. Since we have p h V h L 2 (Ω the dual product Aỹ Ay, p h is well-defined. Let us write the differences of the cost functional as J(ỹ, ũ J(y, u = 1 2 ỹ y d 2 L 2 (Ω + α 2 ũ 2 L 2 (Ω 1 2 y y d 2 L 2 (Ω α 2 u 2 L 2 (Ω + Aỹ ũ Ay + u, p h = 1 2 ỹ y 2 L 2 (Ω α 2 ũ u 2 L 2 (Ω + (αũ + p h, ũ u + A p h + ȳ h y d, ỹ y + (ỹ ȳ h, ỹ y α 2 r ỹ ȳ h 2 L 2 (Ω + (αũ + p h, ũ u + A p h + ȳ h y d, ỹ y. 8 (3.9

10 Now we will estimate the third and fourth addend on the right-hand side. Lemma 3.5 there exists η vi L 2 (Ω such that By (αū h + p h, u ū h α 4 u ū h 2 L 2 (Ω η vi 2 L 2 (Ω u L 2 (Ω : u a u u b. We then obtain (αũ + p h, ũ u = α(ũ ū h, ũ u + (αū h + p h, ũ ū h + (αū h + p h, ū h u α(ũ ū h, ũ u + (αū h + p h, ũ ū h + α 4 u ū h 2 L 2 (Ω + η vi 2 L 2 (Ω α 4 r α ũ ū h L2 (Ω r + α 4 ũ ū h 2 L 2 (Ω ( (αū h + p h, ũ ū h + η vi 2 L 2 (Ω. Due to the complementarity condition (2.10 on µ a,h and the feasibility y a y it holds For analogous reasons we find µ a,h, ỹ y = µ a,h, ỹ ȳ h + ȳ h y a + y a y = µ a,h, ỹ ȳ h + µ a,h, y a y µ a,h, ỹ ȳ h. µ b,h, ỹ y µ b,h, ỹ ȳ h. Thus, the fourth addend in the estimate above can be estimated as A p h + ȳ h y d, ỹ y = A p h + ȳ h y d µ a,h + µ b,h, ỹ y + µ a,h, ỹ y µ b,h, ỹ y A p h + ȳ h y d µ a,h + µ b,h, ỹ y + µ a,h µ b,h, ỹ ȳ h C 0 A p h + ȳ h y d µ a,h + µ b,h H 2 (Ω r + µ a,h µ b,h, ỹ ȳ h, (3.11 where we applied the H 2 (Ω-regularity result Lemma 2.1 in the last step. Combining (3.9 (3.11, we can estimate the difference of the values of the cost functional as J(ỹ, ũ J(y, u α 4 r2 ( α ũ ū h L 2 (Ω + C 0 A p h + ȳ h y d µ a,h + µ b,h H 2 (Ω r ỹ ȳ h 2 L 2 (Ω + α 4 ũ ū h 2 L 2 (Ω + (αū h + p h, ũ ū h + η vi 2 L 2 (Ω which yields the claim with + µ a,h µ b,h, ỹ ȳ h, (3.12 η a := 3 2 α ũ ū h L 2 (Ω + C 0 A p h + ȳ h y d µ a,h + µ b,h H 2 (Ω η b := 1 2 ỹ ȳ h 2 L 2 (Ω + α 4 ũ ū h 2 L 2 (Ω + (αū h + p h, ũ ū h + η vi 2 L 2 (Ω ( µ a,h µ b,h, ỹ ȳ h. 9

11 Remark 3.7. If one uses interior point methods to solve the discretized problem, then the discrete complementarity condition (2.10, in particular µ i,h, ȳ h y i = 0, i {a, b}, is not satisfied in general. It turns out that the estimate of the previous lemma holds true if η a is replaced by η a given by η a := η a + µ a,h, ȳ h y a µ b,h, ȳ h y b, which takes the violation of the discrete complementarity condition into account Upper bound for the error in the control and state. Using the solution of the continuous problem as test functions in Lemma 3.6, we get directly an estimate of the error in the controls. Lemma 3.8. With the notations of the previous Lemma 3.6, it holds ū h ū 2 L 2 (Ω 2 ũ ū h 2 L 2 (Ω + 8 α 2 η2 a + 8 α η b. Proof. By Lemma 3.6, optimality of (ȳ, ū, and feasibility of (ỹ, ũ, we obtain which gives directly 0 J(ỹ, ũ J(y, u α 2 ū ũ 2 L 2 (Ω + η a ū ũ L 2 (Ω + η b, ū ũ 2 L 2 (Ω 4 α 2 η2 a + 4 α η b. The claim follows with ū h ū 2 L 2 (Ω 2( ũ ū h 2 L 2 (Ω + ū ũ 2 L 2 (Ω. Here, the quantity ηa 2 can be bounded from above, cf. (3.13, by η 2 a 9 2 α2 ũ ū h 2 L 2 (Ω + 2C2 0 A p h + ȳ h y d µ a,h + µ b,h 2 H 2 (Ω. (3.14 Now we have everything at hand to derive the upper bound for the discretization error of control and state. Let us emphasize that all quantities on the right-hand side of the error estimate (3.15 are computable. In particular, the right-hand side does not contain any component of the solution (ȳ, ū, p, µ a, µ b of the continuous optimality system. Theorem 3.9. Let (ȳ, ū be the solution of the continuous optimal control problem (1.1 (1.4. Let (ȳ h, ū h, p h, µ a,h, µ b,h be the solution of the discrete problem satisfying (2.7 (2.8, (2.10. Then there is a constant c > 0 that depends only on α, Ω, A, τ such that ( ū h ū 2 L 2 (Ω + ȳ h ȳ 2 L (Ω c r (1 state,l + r(2 state,l + r2 state,l 2 where r (1 state,l + r 2 adjoint,l 2 + r2 control,l 2 + e state is the scaled L -error of the states given by r (1 state,l = ( max ( (αū h + p h, û ū h + µ a,h µ b,h, ŷ ȳ h, 0 10 ( µ a,h µ b,h M(Ω y h ȳ h L (Ω,

12 r (2 state,l is the squared and scaled L -error of the states given by r (2 state,l ( û = ū h 2 L 2 (Ω + ŷ ȳ h 2 L 2 (Ω y h ȳ h 2 L (Ω, r state,l 2 is the L 2 -error of the states given by r state,l 2 = y h ȳ h L2 (Ω, r adjoint,l 2 is the H 2 (Ω-residual in the adjoint equation r adjoint,l 2 = A p h + ȳ h y d µ a,h + µ b,h H 2 (Ω, r control,l 2 is the L 2 (Ω-residual in the variational inequality defined by r control,l 2 = η vi L 2 (Ω, where η vi = η vi (ū h, p h is given by Lemma 3.5, and e state is the weighted violation of the state constraints given by e state = max ( (αū h + p h, û ū h + µ a,h µ b,h, ŷ ȳ h, 0 e sc ( + û ū h 2 L 2 (Ω + ŷ ȳ h 2 L 2 (Ω e 2 sc, where e sc is defined in Lemma 3.1, eq. (3.2. Proof. Let us first combine the results of Lemma 3.6 with the estimate of ηa 2 in (3.14 and the definition of η b in Lemma 3.8, eq. (3.13, to obtain ū h ū 2 L 2 (Ω 40 ũ ū h 2 L 2 (Ω + 16C2 0 α 2 A p h + ȳ h y d µ a,h + µ b,h 2 H 2 (Ω + α( ỹ ȳ h 2 L 2 (Ω + (αū h + p h, ũ ū h + η vi 2 L 2 (Ω + µ a,h µ b,h, ỹ ȳ h. At first, we have by Lemma 3.1, cf. (3.3, and With the help of (3.4 we get (αū h + p h, ũ ū h = (αū h + p h, û ū h σ ũ ū h 2 L 2 (Ω = σ2 û ū h 2 L 2 (Ω. µ a,h µ b,h, ỹ ȳ h = σ µ a,h µ b,h, ŷ ȳ h + (1 σ µ a,h µ b,h, y h ȳ h Similarly, we can estimate using σ 0 σ µ a,h µ b,h, ŷ ȳ h + µ a,h µ b,h M(Ω y h ȳ h L (Ω. ỹ ȳ h 2 L 2 (Ω = σ(ŷ ȳ h + (1 σ(y h ȳ h 2 L 2 (Ω ( 2 σ 2 ŷ ȳ h 2 L 2 (Ω + yh ȳ h 2 L 2 (Ω. 11

13 Hence there is a constant c > 0 depending only on α, Ω, A such that { ū h ū 2 L 2 (Ω c û ū h 2 L 2 (Ω σ2 + A p h + ȳ h y d µ a,h + µ b,h 2 H 2 (Ω + ŷ ȳ h 2 L 2 (Ω σ2 + (αū h + p h, û ū h σ + η 2 L 2 (Ω + µ a,h µ b,h, ŷ ȳ h σ. (3.16 The value of σ can be bounded according to Lemma 3.1 by σ τ 1 ( y h ȳ h L (Ω + e sc, where e sc is the state constraint violation defined in (3.2. Using this estimate in (3.16, the claim follows. In Section 4, we will prove convergence to zero of the upper bound (3.15 if the discrete quantities converge in some sense to solutions of the optimality system in Theorem Localized a-posteriori error estimates. In the previous sections, we developed error bounds for the discretization error. These bounds contain terms that are still not fully accessible. In particular, it needs to be specified, how the L 2 - and L -discretization errors of the states as well as the residual of the adjoint equation can be calculated. For the L 2 -error of the states, we have the following result, which is a standard estimate, see e.g. [4]. Recall that ȳ h is the solution of the discretized equation (2.6 with right-hand side ū h, while y h is the solution of the elliptic equation (3.1 with the same right-hand side ū h. Lemma There is a constant c > 0 depending on Ω, the polynomial degree l, and the shape regularity of the triangulation such that with η 2 state,l 2 = T T η2 T,state,L 2 and Here, y h ȳ h L 2 (Ω c η 2 state,l 2 [ ] ηt,state,l (h 2 = 4 2 T ȳ h + ū h 2 L 2 (T + h3 ȳh 2 T n L 2 ( T \Γ [ ] ȳh n denotes the jump of the normal derivative across interior edges. To estimate the L -error we use the following reliable and efficient error estimator from [19]. Lemma There is a constant c > 0 depending on Ω, the polynomial degree l, and the shape regularity of the triangulation such that y h ȳ h L (Ω c η state,l with η state,l = max T T η T,state,L and ( [ ] η T,state,L = log h min 2 h 2 ȳh T ȳ h + ū h L (T + h T. n L ( T \Γ 12.

14 It remains to describe the estimation of the H 2 -residual of the adjoint equation. For a test function φ V = H 1 0 (Ω H2 (Ω we will employ the Lagrange interpolation I h φ, which has the property φ(x i = (I h φ(x i for all nodes x i. Due to the assumptions on V h we get I h φ V h. Let p h solve the discrete adjoint equation (2.9. We have A p h + ȳ h y d µ a,h + µ b,h H 2 (Ω = sup A p h + ȳ h y d µ a,h + µ b,h, φ φ V, φ H 2 (Ω =1 = sup A p h + ȳ h y d µ a,h + µ b,h, φ I h φ. φ V, φ H 2 (Ω =1 Since µ a,h and µ b,h are a linear combination of Dirac measures concentrated in the mesh nodes, it holds which implies A p h + ȳ h y d µ a,h + µ b,h H 2 (Ω = Following [1] we obtain µ a,h + µ b,h, φ I h φ = 0, sup A p h + ȳ h y d, φ I h φ. φ V, φ H 2 (Ω =1 Lemma There is a constant c > 0 depending on Ω, the polynomial degree l, and the shape regularity of the triangulation such that A p h + ȳ h y d µ a,h + µ b,h H 2 (Ω c η 2 adjoint,l 2 with η 2 adjoint,l 2 = T T η2 T,adjoint,L 2 and [ ] ηt,adjoint,l (h 2 = 4 2 T p h + ȳ h y d 2 L 2 (T + h3 ph 2 T n L 2 ( T \Γ Although this result of [1] is formulated only for n = 2, l = 1, and a single Dirac measure, the proofs carry over one-to-one to the case considered here: n {2, 3}, general FE-space V h with l 1, right-hand side consists of linear combination of Dirac measures concentrated in the nodes. In these Lemmata, we cited only the reliability estimates (i.e. upper error bounds. For all three estimators also lower error bounds (efficiency estimates are available. The localization of the estimator of the error in the variational inequality is an obvious choice. Let us define η 2 T,control,L 2 := η vi T, where η vi = η(ū h, p h is the function constructed in Lemma 3.5. Combining the estimates of this section with the result of Theorem 3.9, we get our main result, which is the localized a-posteriori error estimate. Theorem Let (ȳ, ū be the solution of the continuous optimal control problem (1.1 (1.4. Let (ȳ h, ū h, p h, µ a,h, µ b,h satisfy (2.7 (2.8, (

15 Then there is a constant c > 0 depending on α, Ω, τ, and the shape regularity of the triangulation, and a weight ω,h > 0 such that ū h ū 2 L 2 (Ω + ȳ h ȳ 2 L (Ω c ( η 2 L 2 + ω,h η state,l + e state with η 2 L 2 = T T η2 T,L 2 and η 2 T,L 2 = η2 T,state,L 2 + η2 T,control,L 2 + η2 T,adjoint,L 2. The weight ω,h depends on the discrete quantities (ȳ h, ū h, p h, µ a,h, µ b,h. The mapping from (ȳ h, ū h, p h, µ a,h, µ b,h to ω,h is bounded from C( Ω L 2 (Ω L 2 (Ω M(Ω M(Ω to R. Proof. The result follows directly from Theorem 3.9 and Lemmata 3.10, 3.11, and The quantity ω,h is given by ω,h = max ( (αū h + p h, û ū h + µ a,h µ b,h, ŷ ȳ h, 0 + µ a,h µ b,h M(Ω ( + û ū h 2 L 2 (Ω + ŷ ȳ h 2 L 2 (Ω y h ȳ h L (Ω, cf. the definition of r (1 state,l and r(2 state,l in Theorem 3.9. The mapping ū h y h is bounded from L 2 (Ω to L (Ω by Lemma 2.1, which proves the claimed boundedness of ω,h Marking strategy. It remains to describe, how to mark elements for refinement. Here, we follow the common strategy to mark elements that have relatively large local error indicators. In our case, see Theorem 3.13, the error indicator contains two terms with different accumulation properties: η 2 L 2 = T T η2 T,L 2 and η state,l = max T T (η T,state,L + e T,state. As marking strategy we employ the one used in [19]. Let us define η 2 2 := η 2 L 2 η := ω,h η state,l + e state η := max(η 2, η. We choose an error indicator η i if it is relatively large compared to the total error, that is if η i θ 1 η, i {2, }. For a chosen error indicator η i, we mark elements by the maximum strategy, that is, elements ˆT with η ˆT,i θ 2 max T Th η T,i are marked for refinement. Here, the parameters θ 1, θ 2 are taken from (0, 1. In our computations we used θ 1 = 0.2, θ 2 = Convergence of error bound. In this section we will prove the convergence to zero of the error bound of Theorem 3.9 if the solution of the discrete system converges in the following sense. Assumption 4. Let a sequence of meshes T k with associated solutions of the discrete problem {(ȳ hk, ū hk, p hk, µ a,hk, µ b,hk } be given. Let us assume that we have the following properties of this sequence: (i The sequence (ȳ hk, ū hk converges strongly to (ȳ, ū in C( Ω L 2 (Ω. (ii The sequence ( p hk, µ a,hk, µ b,hk is bounded in W 1,q (Ω M(Ω M(Ω with 2n 2+n < q < n n 1. 14

16 (iii For each subsequence ( p hk, µ a,hk, µ b,hk with p hk p in W 1,q (Ω, q > 2n 2+n, and ( µ a,hk, µ b,hk ( µ a, µ b in M(Ω M(Ω the limit element ( p, µ a, µ b is a Lagrange multiplier to (ȳ, ū, i.e. the system (2.2 (2.4 is satisfied. Please note, that the requirements of this assumption are satisfied for a uniform refinement of the mesh, see e.g. [13, Chapter 3]. However, convergence of the mesh size h 0 is not explicitly required. Let us recall the error representation of Theorem 3.9, which reads ( ū h ū 2 L 2 (Ω + ȳ h ȳ 2 L (Ω c r (1 state,l + r(2 state,l + r2 state,l 2 + radjoint,l r2 control,l + e 2 state. Lemma 4.1. Let Assumption 4 be satisfied. Then it holds for k. r (1 state,l + r(2 state,l + r2 state,l 2 + r2 adjoint,l 2 + e state 0 Proof. Let us define y h k to be the solution of (3.1 to the control ū hk. Then we have which proves that r (1 the Assumption 4. y h k ȳ hk L (Ω y h k ȳ L (Ω + ȳ ȳ hk L (Ω C ū hk ū L 2 (Ω + ȳ ȳ hk L (Ω. state,l, r(2 state,l, and similarly r state,l2 converge to zero under Because of y hk ȳ in C( Ω and the feasibility of ȳ with respect to the control constraints, the state constraint violation e sc and consequently e state tend to zero. Let weak (weak converging subsequences ( p hk, µ a,hk, µ b,hk be given. Then by compact embeddings and after extracting another subsequence p hk p in L 2 (Ω and ( µ a,hk, µ b,hk ( µ a, µ b in H 2 (Ω. Hence r adjoint,l 2 0 for this subsequence. Since the subsequence (k was chosen arbitrary, it follows r adjoint,l 2 0 for k. The discussion of the estimator r control,l 2 is more involved, and thus we state and prove it separately. For convenience, let us recall its definition. In Theorem 3.9 we set r control,l 2 := η vi L2 (Ω, where η vi was given by Lemma 3.5 and defined by η 2 vi = 1 α χ Ω 0,h (αū h + p h 2 + χ Ωa,h (αū h + p h (ū h u a + χ Ωb,h (αū h + p h (ū h u b, where the sets Ω 0,h, Ω a,h, Ω b,h were defined as { Ω 0,h = x Ω : ū h (x, 1 α p h(x (u a, u b or ū h (x = u a, αū h (x + p h (x < 0 } or ū h (x = u b, αū h (x + p h (x > 0 and Ω a,h = {x : ū h (x (u a, u b, 1α } p h(x u a Ω b,h = {x : ū h (x (u a, u b, 1α } p h(x u b. 15

17 Lemma 4.2. Let Assumption 4 be satisfied. Then it holds r control,l 2 0. Proof. Since the controls u hk are feasible for the discrete problem, they are uniformly bounded in L (Ω. Hence, the sequence u hk converges to ū in L s (Ω for all 2 s <. Moreover, as argued above, each weakly converging subsequence p hk has a strongly converging subsequence p hk p in L 2 (Ω. By Assumption 4, we have that the variational inequality (2.4 is satisfied. In the course of the proof, we will bound η vi L2 (Ω in terms of p hk p L2 (Ω and ū hk ū L s (Ω, s > 2. To simplify the notation, we will drop the index k. At first, let us note that η vi (x 2 1 α αū h(x + p h (x 2 (4.1 follows directly from the definition of η vi, Ω a,h, and Ω b,h, confer also (3.7. Now, we will discuss upper bounds of η vi on different subsets of Ω. Case (1: Ω 1 = {x Ω : αū(x + p(x = 0}. Using (4.1 it follows directly η vi 2 L 2 (Ω 1 α 1 αū h + p h (αū + p 2 L 2 (Ω 1 α 1 (α ū h ū L 2 (Ω + p h p L 2 (Ω 2. (4.2 Case (2 a : Ω 2,a = {x Ω : ū h (x = ū(x = u a }. Here, we have η vi 2 L 2 (Ω 2,a = α 1 (αū h + p h 2 L 2 (Ω 2,a = α 1 (αūh + p h (αū + p 2 L 2 (Ω 2,a α 1 (α ū h ū L 2 (Ω + p h p L 2 (Ω 2. Case (2 b : Ω 2,b = {x Ω : ū h (x = ū(x = u b }. Analogous to Case (2 a. Case (3 a : Ω 3,a = {x Ω : ū(x = u a } Ω a,h. Here, we have by definition of η vi η vi (x 2 = (αū h + p h (ū h u a = (αū h + p h (ū h ū. Hence, it holds η vi 2 L 2 (Ω 3,a αū h + p h L2 (Ω ū h ū L2 (Ω. Case (3 b : Ω 3,b = {x Ω : ū(x = u b } Ω b,h. Analogous to Case (3 a. Case (4 a : Ω 4,a = {x Ω : ū h (x, 1 α p h(x (u a, u b, ū(x = u a, 1 α p(x < u a}. The inequality 1 α p(x < u a = ū(x < 1 α p h(x implies αū h + p h α ū h ū + α ū ( α 1 p h α ū h ū + p ( p h which proves with (4.1 η vi 2 L 2 (Ω 4,a 1 α αū(x + p(x 2 L 2 (Ω 4,a p h p 2 L 2 (Ω 4,a. 16

18 Case (4 b : Ω 4,b = {x Ω : ū h (x, 1 α p h(x (u a, u b, ū(x = u b, 1 α p(x < u b}. Analogous to Case (4 a. Case (5 a : Ω 5,a = {x Ω : ū(x = u a } Ω b,h. Here, we have 1 α p(x u a < u b < 1 α p h(x. By Chebyshev s inequality, we obtain Ω 5,a { x : α 1 p h (x p(x u b u a } p h (x p(x Ω\Ω2 2 α 2 u b u a 2 1 (α u b u a 2 p h p 2 L 2 (Ω. Together with the definition η vi on Ω b,h, we find η vi 2 L 2 (Ω 5,a Ω 5,a 1/2 αū h + p h L2 (Ω ū h u b L (Ω c p h p L2 (Ω. Case (5 b : Ω 5,b = {x Ω : ū(x = u b } Ω a,h. Analogous to Case (5 a. Case (6: Ω 6 = {x Ω : ū(x, ū h (x {u a, u b }, ū(x ū h (x}. That is, here the control constraints are active at ū and ū h but both are not equal. Similarly as in Case (5 a we estimate Ω 6 { x : ū(x ū h (x u b u a } ū h (x ū(x Ω\Ω2 s u b u a s which yields due to Ω 6 Ω 0,h 1 u b u a s ū h ū s L s (Ω, η vi 2 L 2 (Ω 6 1 α Ω 6 1 2/s αū h + p h 2 L s (Ω C ū h ū s 2 L s (Ω. Let us argue that the splitting introduce by the cases above covers Ω. Due to first order optimality conditions, Ω can be divided in sets, where αū + p = 0 and ū {u a, u b }. The first possibility is covered by Case (1. The case that both ū and ū h are at the bounds is contained in Case (2 and Case (6. Now it remains to cover the set, where ū {u a, u b } and ū h (u a, u b. The subset, where 1 α p h is not in (u a, u b, is discussed in Case (3 and Case (5. And Case (4 covers the subset, where 1 α p h is in (u a, u b. Summing up all the estimates, we find the convergence η vi 0 in L 2 (Ω for the subsequence (k chosen above. This implies that for every subsequence of (ū hk, p hk we can choose a subsequence such that the corresponding quantity η vi converges to zero, which finishes the proof. As a consequence of these results we obtain the main result of this section. Theorem 4.3. Let Assumption 4 be satisfied. Then the error bound given by Theorem 3.9 converges to zero for k, e.g. ( r (1 state,l + r(2 state,l + r2 state,l + 2 r2 adjoint,l + 2 r2 control,l + e 2 state 0. 17

19 Let us now comment on different available error estimators for the error in the variational inequality connected to the control constraints. To the best of our knowledge, all estimators in the literature contain in their upper bounds quantities that do not tend to zero as (ū h, p h converges, which makes it difficult to prove a convergence result similar to Lemma 4.2. Let us first compare our findings to the error estimator as considered by Krumbiegel and Rösch [15]. Their error estimator η KR coincides with η vi as developed here except for the definition on the sets Ω a,h, Ω b,h, i.e. η KR = χ Ω0,h η vi + 1 α χ Ω a,h Ω b,h (αu h + p h 2. which implies η vi 2 L 2 (Ω η KR 2 L 2 (Ω by (3.7. The error estimator η KR does not allow a convergence proof comparable to Lemma 4.2. To this end consider the following example: let the constant functions u a = 0, u b = 1, α = 1, ū = 0, p = p h = 1, and ū h = h be given. That means, the values are chosen in such a way that Ω a,h = Ω and correspond to the Case (3 a in the proof of Lemma 4.2. Our error estimator gives η vi 2 L 2 (Ω = (αū h + p h, ū h u a = (αh + 1 h Ω 0 as h 0. The estimator of [15] yields η KR 2 L 2 (Ω = 1 α αh L 2 (Ω, which does not converge to zero as h 0. Similar situations as in the example will occur if one uses interior point methods to get rid of the control constraints in the discrete optimization problem. Second, let us comment on the error estimator analyzed by Hintermüller, Hoppe, Iliash, and Kieweg in [11]. There an efficient and reliable error estimator for optimal control problems with a lower control bound is developed. They prove that the estimator is equivalent (up to higher order terms to the error ȳ ȳ h H 1 (Ω + p p h H 1 (Ω + ū ū h L 2 (Ω + σ σ h L 2 (Ω, with σ := αū + p and σ h := αū h + Π h p h. However, it is not clear, whether it holds σ σ h L 2 (Ω 0 if (u h, p h converges to (ū, p. Indeed, for Case (3 a in the proof of Lemma 4.2 one has the following. If (u h, p h are solutions of the discrete optimal control problem it holds αu h + Π h p h = 0 on this set, since u h is not at the control bounds. This implies that σ σ h L2 (Ω = (αū + p (αu h + Π h p h L2 (Ω 3,a = αū + p L2 (Ω 3,a. However, since ū = u a holds on Ω 3,a by definition, this quantity is in general non-zero. In order to prove σ σ h L2 (Ω 0, one has to prove in addition that the measure of Ω 3,a tends to zero. Finally, we comment on the error estimator for control constrained optimal control problems as considered by Li, Liu, and Yan [16]. The error estimator developed there converges to zero under the assumption that (ȳ h, ū h, p h converges to (ȳ, ū, p, an assumption on the regularity of the active sets, and the assumption h 0. Clearly our convergence result Lemma 4.2 holds under weaker assumptions on ū h, p h. 5. Numerical experiment. Let us report on numerical results with adaptive refinement using the error estimator developed in the present article. The data of the example is taken from [6]. It was originally posed for Ω = B 1 (0. We modified it to work with Ω = [ 1, 2] 2. 18

20 The data of the problem is given in polar coordinates. For convenience we define r = x x2 2. Let us set ū(r = 1 2πα χ {r 1}(log r + r 2 r 3 ȳ(r = 1 ( r 2 2πα χ r3 {r 1} (log r p(r = αū(r µ a (r = δ 0 (r f(r = 1 8π χ {r 1}(4 9r + 4r 2 4r 3 y d (r = ȳ(r + 1 2π χ {r 1}(4 9r y a (r = 1 ( 1 2πα 4 r. 2 The problem features one lower state constraint, there are no upper state constraint and no control constraints given. One can verify that (ȳ, ū, p, µ a is the solution of the problem: Minimize J(y, u given by subject to and Moreover, one can verify that J(y, u = 1 2 y y d 2 L 2 (Ω + α 2 u 2 L 2 (Ω y = u + f in Ω y = 0 on Γ, y a y in Ω. û(r = χ {r 1} ( r ŷ(r = χ {r 1} (1 + 3r 2 2r 3 fulfills Assumption 2 with τ = Due to the special structure of the problem, the dual quantities are uniquely determined. Please note, that the adjoint state has a pronounced singularity. Since the data of the problem are smooth, there is no chance to perform an a-priori mesh refinement to resolve the singularity. To avoid superconvergence effects, we ensured that the point x = (0, 0 cannot be a node of the grid for any refined mesh. This is achieved with a coarse grid mesh generated as a uniform triangulation with 8 triangles. Figure 5.1 shows the adaptively refine mesh after 5 refinement steps. It shows local refinement around the singularity at (0, 0, which appears only in the adjoint equation and thus is not identifiable a-priori. The right-hand plot compares the error ū h ū L 2 (Ω for adaptive and uniform mesh refinement. Clearly, the adaptive mesh refinement leads to a better approximation of the solution with respect to the number of unknowns. 19

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